Rev Neurosci. 2013;24(5):537-52. doi: 10.1515/revneuro-2013-0032.
Here, we present a state-of-the-art review of the research performed on the brain-computer interface (BCI) technologies with a focus on signal processing approaches. BCI can be divided into three main components: signal acquisition, signal processing, and effector device. The signal acquisition component is generally divided into two categories: noninvasive and invasive. For noninvasive, this review focuses on electroencephalogram. For the invasive, the review includes electrocorticography, local field potentials, multiple-unit activity, and single-unit action potentials. Signal processing techniques reviewed are divided into time-frequency methods such as Fourier transform, autoregressive models, wavelets, and Kalman filter and spatiotemporal techniques such as Laplacian filter and common spatial patterns. Additionally, various signal feature classification algorithms are discussed such as linear discriminant analysis, support vector machines, artificial neural networks, and Bayesian classifiers. The article ends with a discussion of challenges facing BCI and concluding remarks on the future of the technology.
在这里，我们对脑机接口（BCI）技术的研究进行了最新综述，重点介绍了信号处理方法。BCI 可以分为三个主要组成部分：信号采集、信号处理和效应器设备。信号采集组件通常分为两类：非侵入式和侵入式。对于非侵入式，本综述重点介绍脑电图。对于侵入式，综述包括脑电描记术、皮质电图、局部场电位、多单位活动和单单位动作电位。回顾的信号处理技术分为时频方法，如傅里叶变换、自回归模型、小波和卡尔曼滤波器，以及时空技术，如拉普拉斯滤波器和公共空间模式。此外，还讨论了各种信号特征分类算法，如线性判别分析、支持向量机、人工神经网络和贝叶斯分类器。文章最后讨论了 BCI 面临的挑战，并对该技术的未来进行了总结。
